Grid planning method for improving multi-dimensional stable supporting capability of receiving-end power grid

Abstract

The invention provides a grid planning method for improving the multi-dimensional stable supporting capability of a receiving-end power grid, and the method comprises the following steps: obtaining a main grid node and a grid line data set, and obtaining a simplified model of an AC system for accessing new energy through employing a multi-port Thevenin equivalent method, constructing a receiving-end power grid frame planning model, and solving by adopting NSGA-II to obtain a planning scheme set; based on the planning scheme set, respectively calculating to obtain an evaluation index value set; and based on the planning scheme set and the evaluation index value set, screening the planning scheme set by adopting a TOPSIS method of an entropy weight method to obtain an optimal planning scheme. According to the grid planning method for improving the multi-dimensional stable supporting capacity of the receiving-end power grid, the running state of the power grid can be monitored in real time, the anti-interference capacity of the network is evaluated, the planning strategy is adjusted, and therefore the admitting capacity of new energy is improved.

Description

Technical Field

[0001] The present invention relates to the field of power systems and automation technologies thereof, and in particular to a grid planning method for improving the multi-dimensional stable supporting capability of a receiving-end power grid. Background Art

[0002] In recent years, new energy sources such as wind power and photovoltaics have grown rapidly. New energy and DC have replaced conventional units in large quantities. Interactive energy-consuming equipment such as electric vehicles, distributed energy, and energy storage have been widely used. The power system presents the "double high" characteristics of a high proportion of renewable energy and a high proportion of power electronic equipment. With the proposal of the "carbon peak and carbon neutrality" goal vision, higher requirements are put forward for the safety and operation stability of the power system. Different from the traditional power system dominated by synchronous machines, the new energy sites dominated by wind power and photovoltaics have the characteristics of decentralization, weak support, weak anti-interference and low inertia. In addition, the large-scale new energy feed-into the receiving-end power grid has greatly weakened the voltage and power angle stability support capacity of the receiving-end power grid. When accidents such as DC commutation failure and new energy disconnection occur, the voltage and power angle stability problems of the system will further deteriorate, and even cause the system to collapse in severe cases. Increasing and expanding the power grid of the power system can improve the strength of the power grid and the effective utilization rate of new energy, and enhance the power grid's ability to regulate the uncertainty of new energy. Therefore, it is of great significance to study a reasonable receiving-end power grid grid planning method and improve the stable support capacity of the receiving-end power grid under large-scale new energy access.

[0003] Currently, in the research on receiving-end power grid planning methods, there are relatively few methods for receiving-end power grid planning with a high proportion of multiple renewable energy sites. Most of them are aimed at scenarios of renewable energy sites and transmission network planning, and there is still insufficient research on the impact of renewable energy grid connection on the weakening of the supporting capacity of the receiving-end power grid. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a grid planning method that can monitor the operating status of the power grid in real time, evaluate the network's anti-interference capability and adjust the planning strategy, thereby improving the acceptance capacity of new energy and enhancing the multi-dimensional stable supporting capacity of the receiving power grid.

[0005] To achieve the above object, the technical solution adopted by the present invention is as follows: a grid planning method for improving the multi-dimensional stable support capability of a receiving-end power grid, comprising the following steps:

[0006] Based on the new energy grid-connected system, a main grid node and grid line data set are obtained; the grid line data set includes an original line data set and a line data set to be planned;

[0007] A simplified model of an AC system for connecting new energy sources is obtained based on the main grid nodes and the grid line data set using a multi-port Thevenin equivalent method;

[0008] A receiving-end power grid framework planning model is constructed based on the simplified model of the AC system, wherein the receiving-end power grid framework planning model includes decision variables and objective functions, wherein the decision variables are the line data set to be planned, and the objective function includes a cost minimization function and an evaluation index maximization function of the line data set to be planned, and the evaluation index is an index for measuring the stability of the power grid;

[0009] Using NSGA-II to solve the receiving-end power grid planning model to obtain a planning scheme set;

[0010] Based on the planning scheme set, according to the simplified model of the AC system and the new energy grid-connected system, respectively calculate and obtain an evaluation index value set;

[0011] Based on the planning scheme set and the evaluation index value set, the TOPSIS method of the entropy weight method is used to screen the planning scheme set to obtain the optimal planning scheme.

[0012] Furthermore, the grid stability includes voltage stability, frequency stability and power angle stability.

[0013] Furthermore, the steps of obtaining the simplified model of the AC system are:

[0014] Obtaining a node-line association matrix and a line admittance matrix according to the main grid nodes and the original line data set;

[0015] Obtaining an original line vector according to the original line data set;

[0016] Obtaining a route vector to be planned according to the route data set to be planned;

[0017] Obtaining a first diagonal matrix by calculating a diagonal matrix after adding the original line vector and the line vector to be planned;

[0018] Obtaining a second diagonal matrix by calculating a diagonal matrix according to the line admittance matrix;

[0019] Obtaining a node-line transposed matrix according to the node-line association matrix transposed;

[0020] Obtaining a node admittance matrix by multiplying the node-line association matrix, the first diagonal matrix, the second diagonal matrix, and the node-line transposed matrix;

[0021] Obtaining an original impedance matrix by inverting the node admittance matrix;

[0022] The simplified model of the AC system is obtained according to the original impedance matrix using a multi-port Thevenin equivalent method.

[0023] Furthermore, the cost of the route data set to be planned includes a one-time investment fee and an operation and maintenance fee.

[0024] Furthermore, the cost minimization function is:

[0025]

[0026] In the formula, min is the minimization function, f1 is the cost function, C1 is the primary investment cost, C2 is the operation and maintenance cost, x is the line vector to be planned, l is the distance vector of the transmission line, r is the distance between the transmission lines and the transmission lines. D is the discount rate, S L For the service life.

[0027] Furthermore, based on the evaluation index value set, the steps of using the TOPSIS method of the entropy weight method to screen the planning scheme set to obtain the optimal planning scheme are as follows:

[0028] Constructing an original evaluation matrix based on the planning scheme set and the evaluation index value set, wherein the number of rows of the original evaluation matrix is ​​the number of planning schemes in the planning scheme set, and the number of columns of the original evaluation matrix is ​​the types of evaluation indicators in the evaluation index value set;

[0029] Performing forward and standardization processing on the original evaluation matrix to obtain an evaluation matrix;

[0030] Obtaining an indicator weight value set according to the evaluation matrix, where the indicator weight is the ratio of an evaluation indicator under one planning scheme to the sum of the same evaluation indicators under all planning schemes;

[0031] Obtaining an information entropy value set according to the indicator weight value set, wherein the information entropy value set includes the information entropy of the same evaluation indicator;

[0032] Obtaining a weight value set according to the information entropy value set, wherein the weight value set includes weights of the same evaluation indicator;

[0033] Performing weighted processing on the evaluation matrix according to the weight value set to obtain a weighted evaluation matrix, wherein the weighted evaluation matrix includes weighted evaluation values;

[0034] Obtaining maximum and minimum values ​​according to the weighted evaluation matrix;

[0035] Obtaining a first distance value set according to the weighted evaluation matrix and the maximum value, wherein the first distance is the distance between the weighted evaluation value and the maximum value;

[0036] Obtaining a second distance value set according to the weighted evaluation matrix and the minimum value, wherein the second distance is the distance between the weighted evaluation value and the minimum value;

[0037] Obtaining an evaluation value set of the planning scheme set according to the first distance value set and the second distance value set;

[0038] Sorting the planning schemes according to the evaluation value set and obtaining a sorting result;

[0039] The optimal planning scheme is obtained according to the sorting result.

[0040] Furthermore, the evaluation value is:

[0041]

[0042] In the formula, S a is the evaluation value of the a-th planning scheme, is the first distance of the a-th planning scheme, is the second distance of the a-th planning scheme, and a is the sequence number of the planning scheme in the planning scheme set.

[0043] Furthermore, the evaluation index value set includes a short-circuit ratio improvement margin value set, a damping ratio improvement margin value set and a transient power transmission capacity improvement margin value set.

[0044] Furthermore, the short circuit ratio improvement margin is obtained according to the short circuit ratio of the new energy station and the initial short circuit ratio; the initial short circuit ratio is the initial short circuit ratio of the new energy grid-connected system;

[0045] The short-circuit ratio of the new energy station is:

[0046]

[0047] Where, MRSCR i is the short-circuit ratio of the i-th renewable energy station, S aci is the three-phase short-circuit capacity of the grid-side access point corresponding to the i-th new energy station, P rei is the active power injected into the grid connection point of the i-th renewable energy station, Z eqij is the equivalent impedance from the jth renewable energy station to the ith renewable energy station, Z eqii is the equivalent self-impedance of the i-th new energy station, P rej is the active power injected into the grid connection point of the j-th renewable energy station, i and j are the serial numbers of the renewable energy stations, and n is the total number of renewable energy stations.

[0048] A device for implementing the grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid, comprising a data input unit, a data analysis unit and an optimization algorithm unit;

[0049] The data input unit:

[0050] Used to obtain main grid node and grid line data sets based on new energy grid-connected systems;

[0051] Used to obtain a simplified model of the AC system for connecting new energy sources according to the main grid nodes and the grid line data sets by using a multi-port Thevenin equivalent method;

[0052] The data analysis unit:

[0053] Used to construct a receiving-end power grid planning model based on the simplified model of the AC system;

[0054] Used to solve the receiving-end power grid planning model using NSGA-II to obtain a planning solution set;

[0055] The optimization algorithm unit:

[0056] Used to calculate and obtain evaluation index value sets based on the planning scheme set, according to the simplified model of the AC system and the new energy grid-connected system;

[0057] It is used to screen the planning scheme set based on the planning scheme set and the evaluation index value set by using the TOPSIS method of the entropy weight method to obtain the optimal planning scheme.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention traverses all possible line planning schemes and screens out the optimal planning scheme. The present invention uses the location and number of planned lines as decision variables and comprehensively evaluates the stable support capacity of the receiving power grid under each planning scheme by using the grid stability index, which is conducive to a more comprehensive and balanced evaluation of the planning scheme and improves the stability of the power system.

[0060] 2. The present invention uses a non-dominated sorting genetic algorithm with an elite strategy to solve and obtain the Pareto solution set. The fast non-dominated sorting algorithm is adopted to reduce the computational complexity, and the elite strategy is introduced to expand the sampling space. By combining the parent population with the offspring population it produces, the competition mechanism is enhanced, ensuring that excellent genes are retained, thereby improving the accuracy of the optimization results. At the same time, all individuals in the population are stored in layers, ensuring the existence of the best individuals and rapidly improving the overall level of the population. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a flow chart of a grid planning method for improving the multi-dimensional stable support capability of a receiving-end power grid according to the present invention;

[0062] Figure 2 It is a wiring diagram of a DC feed-in system containing a high proportion of renewable energy in a certain area in the calculation example of the present invention;

[0063] Figure 3 The Pareto optimal frontier graph is obtained by using the non-dominated sorting genetic algorithm with an elite strategy in the example of the present invention;

[0064] Figure 4 It is a power angle curve diagram of a generator with a three-phase short circuit fault under the original grid scheme in the calculation example of the present invention;

[0065] Figure 5 It is a node voltage curve diagram of a three-phase short circuit fault under the original grid scheme in the calculation example of the present invention;

[0066] Figure 6 It is a power angle curve diagram of a generator with a three-phase short circuit fault under planning scheme 4 in the calculation example of the present invention;

[0067] Figure 7 It is a node voltage curve diagram of a three-phase short circuit fault under planning scheme 4 in the calculation example of the present invention;

[0068] Figure 8 It is a power angle curve diagram of the generator with a three-phase short circuit fault under the planning scheme 7 in the calculation example of the present invention;

[0069] Fig. 9 It is a node voltage curve diagram of a three-phase short circuit fault under planning scheme 7 in the calculation example of the present invention;

[0070] Fig.10 It is a power angle curve diagram of a generator with a three-phase short circuit fault under the optimal planning scheme in the calculation example of the present invention;

[0071] Fig.11 It is a node voltage curve diagram of a three-phase short circuit fault under the optimal planning scheme in the calculation example of the present invention. DETAILED DESCRIPTION

[0072] In order to clearly illustrate the technical features of this solution, this solution is described below through a specific implementation method.

[0073] The research on the receiving-end power grid planning method considering factors such as high proportion of multiple renewable energy stations is of great significance, which is conducive to promoting the consumption of renewable energy and ensuring the safe and stable operation of new power systems. Figure 1 The present embodiment provides a grid planning method for improving the multi-dimensional stable support capability of a receiving-end power grid, comprising the following steps:

[0074] Based on the new energy grid-connected system, main grid nodes and grid line data sets are obtained; the grid line data set includes an original line data set and a line data set to be planned; the set of existing (before planning) grid lines in the new energy grid-connected system is the original line set, and the original line data set is obtained according to the original line set; the steps of obtaining the line data set to be planned are: based on the new energy grid-connected system, the original main grid nodes are first extracted, all the original main grid nodes are freely combined into initial grid lines in pairs, and the unreasonable lines in the initial grid lines are discarded in consideration of actual geographical factors and conditions, and the main grid nodes and grid lines to be planned are formed after the passive nodes in the new energy grid-connected system are eliminated, and the line data set to be planned is obtained according to the grid lines to be planned;

[0075] The simplified model of the AC system is obtained based on the main grid node data set and the grid line data set using the multi-port Thevenin equivalent method. The steps for obtaining the simplified model of the AC system are as follows:

[0076] According to the main grid nodes and the original line data set, the node line association matrix A and the line admittance matrix y are obtained. b ;

[0077] Obtain the original line vector x0 according to the original line data set;

[0078] Obtaining a route vector x to be planned according to the route data set to be planned;

[0079] The first diagonal matrix is ​​obtained by adding the original line vector x0 and the line vector x to be planned, and the first diagonal matrix is ​​diag(x+x0);

[0080] According to the line admittance matrix, the diagonal matrix is ​​obtained to obtain the second diagonal matrix diag(y b );

[0081] Obtain a node-line transposed matrix according to the node-line association matrix transposed;

[0082] The node admittance matrix is ​​obtained by multiplying the node-line association matrix, the first diagonal matrix, the second diagonal matrix and the node-line transposed matrix. The node admittance matrix is: Y = Adiag (x + x0) diag (y b )A T ;

[0083] The original impedance matrix is ​​obtained by inverting the node admittance matrix. The original impedance matrix is ​​Z = Y -1 ;

[0084] The multi-port Thevenin equivalent method is used to obtain a simplified model of the AC system that connects new energy sources based on the original impedance matrix; the steps are:

[0085] Assume that the new energy grid-connected system is an electric power network including N (N>2n) main grid nodes, the main grid nodes include n grid-connected nodes of new energy sites, and n non-new energy grid-connected nodes corresponding to the grid-connected nodes of n new energy sites, and the non-new energy sites are equivalent; the grid-connected nodes of n new energy sites and n non-new energy grid-connected nodes form n ports, each port corresponds to a group of grid-connected nodes of new energy sites and grid-connected nodes of non-new energy; perform multi-port Thevenin equivalent on the power network from n ports, then:

[0086]

[0087] Where M i is the association vector of the node port corresponding to the i-th new energy station, element 1 represents the port node entry point, and -1 represents the port node exit point. L is the node-port association matrix. eq is the equivalent impedance matrix, which is transformed from the original impedance matrix Z. eq is the equivalent power source of the main grid of the power network, V is the node port voltage before equivalent;

[0088] Equivalent impedance matrix Z eq for:

[0089]

[0090] Assume that the AC current injected by each renewable energy grid-connected busbar is expressed as Then the voltage of each grid-connected bus node It can be expressed as:

[0091]

[0092] The receiving-end power grid planning model is constructed based on the simplified model of the AC system. Figure 1 The multi-objective receiving-end power grid planning model includes decision variables and objective functions. The decision variables are the data sets of lines to be planned. The data sets of lines to be planned include the locations and quantities of lines to be planned. The objective functions include the cost minimization function of the data sets of lines to be planned and the evaluation index maximization function. The evaluation index is an indicator for measuring the stability of the power grid. The power grid stability includes voltage stability, frequency stability and power angle stability.

[0093] The cost of the route data set to be planned includes the one-time investment cost and the operation and maintenance cost, that is, the total investment and operation cost; the cost minimization function is:

[0094]

[0095] In the formula, min is the minimization function, f1 is the cost function, C1 is the primary investment cost, C2 is the operation and maintenance cost, x is the line vector to be planned, l is the distance vector of the transmission line, r is the distance between the transmission lines and the transmission lines. D is the discount rate, S L is the service life;

[0096] The evaluation index value set includes a short-circuit ratio improvement margin value set, a damping ratio improvement margin value set and a transient transmission capacity improvement margin value set; the short-circuit ratio improvement margin is obtained according to the short-circuit ratio of the new energy station and the initial short-circuit ratio MRSCR0; the initial short-circuit ratio MRSCR0 is the initial short-circuit ratio of the new energy grid-connected system, which can be obtained by solving the following formula of the short-circuit ratio of the new energy station based on the new energy grid-connected system before planning; the short-circuit ratio of the new energy station is:

[0097]

[0098] Where, MRSCR i is the short-circuit ratio of the i-th renewable energy station, S aci is the three-phase short-circuit capacity of the grid-side access point corresponding to the i-th new energy station, P rei is the active power injected into the grid connection point of the i-th renewable energy station, Z eqij is the equivalent impedance from the jth renewable energy station to the ith renewable energy station, Z eqii is the equivalent self-impedance of the i-th new energy station, P rej is the active power injected into the grid connection point of the j-th renewable energy station, i and j are the serial numbers of the renewable energy stations, and n is the total number of renewable energy stations;

[0099] The short-circuit ratio of new energy stations takes into account the amplitude and phase difference of each electrical quantity between different nodes, and can take into account the impact of the reactive power of new energy power generation equipment. It is suitable for the voltage strength assessment and calculation of multiple new energy stations connected to the system in various scenarios. According to the short-circuit ratio of new energy stations, the strength level of the new energy access AC system is divided. It is considered that the system with a short-circuit ratio of multiple new energy stations at the grid connection point of more than 3.0 is a strong system, the system between 2.0-3.0 is a weak system, and the system below 2.0 is an extremely weak system.

[0100] The evaluation index is a power grid stability index. Preferably, the evaluation index maximization function is a short-circuit ratio improvement margin maximization function, and the short-circuit ratio improvement margin maximization function is:

[0101]

[0102] In the formula, max is the maximization function, and f2 is the short-circuit ratio improvement margin function;

[0103] The receiving-end power grid planning model also includes constraints, which include power flow equation constraints, line capacity constraints, voltage constraints, power angle constraints, and active output constraints; the constraints are:

[0104]

[0105] V i min ≤V i ≤V i max ;

[0106]

[0107] Where P G is the active power vector output by the new energy station, P D is the active power vector absorbed by the receiving load, P line is the active power vector transmitted on the transmission line, A is the node-branch correlation matrix, y b is the branch admittance matrix, θ is the node phase vector of the system, and T represents transpose, is the minimum active power vector output by the i-th new energy station, P Gi is the active power vector output by the i-th new energy station, is the maximum active power vector output by the i-th new energy station, V i min is the minimum voltage of the i-th new energy station, V i is the voltage of the i-th new energy station, V i max is the maximum voltage of the i-th new energy station, is the minimum active power vector transmitted on the transmission line, is the maximum active power vector transmitted on the transmission line, is the minimum power angle of the i-th new energy station, θ i is the power angle of the i-th new energy station, is the maximum power angle of the i-th new energy station;

[0108] In addition, the steps to calculate the damping ratio are:

[0109] S01. The dynamic characteristics of the power system can be described by a set of nonlinear differential equations and a set of nonlinear algebraic equations:

[0110]

[0111] In the formula, x iis the state variable or algebraic variable of the system. In the power system, it may be the rotor angle, current, voltage and other variables of the generator. f(x1, x2…, x n ) is the dynamic equation of the state variable, describing the process of the state variable changing with time, g(x1, x2…, x n ) is an algebraic equation that describes the equilibrium constraints of the system, such as the power flow equation, etc., m is the number of state variables, and n is the number of total variables;

[0112] S02. Linearize the equations near the stable point and express them in matrices as follows:

[0113]

[0114] Among them, J is the system linearization matrix, A, B, C, and D are the matrices obtained by taking partial derivatives of state variables and algebraic variables after linearizing the system nonlinear equations, ΔX is the state differential variable, and ΔY is the algebraic variable.

[0115] S03. Eliminating non-state variables, we can obtain the state equation describing the linear system:

[0116]

[0117] S04. In the state equation, A' is the state matrix of the system:

[0118]

[0119] S05. Call the QR algorithm to find the eigenvalues ​​of the state matrix A':

[0120] λ i =a i ±jw i ;

[0121] In the formula, λ i is the eigenvalue of the state matrix A', α i is the attenuation coefficient, the unit is 1 / s, w i is the oscillation angular frequency, in rad / s;

[0122] S06. The damping ratio is obtained from the characteristic value of the system oscillation:

[0123]

[0124] The formula for calculating transient power transfer capability is:

[0125]

[0126] Where U mτ , U nτ is the voltage amplitude of the nodes at both ends of branch τ, Δθτ is the voltage phase difference between the two ends of branch τ, τ is the grid line number, N is the total number of grid lines, and the calculation formula of k is used to screen and identify the key branch k with the minimum index value, and the corresponding sBTTC k It can quantitatively characterize the deterioration of power angle stability and voltage stability. As the system stability deteriorates, the sBTTC of key branches k It has the characteristic of monotonically decreasing;

[0127] NSGA-II is used to solve the receiving-end power grid planning model to obtain a Pareto solution set, and φ planning schemes are selected from the frontier of the Pareto solution set or φ planning schemes with a ranking level of 1 are selected from the Pareto solution set to obtain a planning scheme set, that is, there are φ planning schemes in the planning scheme set, φ≤n, and each new energy site corresponds to a planning scheme; according to the planning scheme set, φ group evaluation index values ​​are obtained, and the φ group evaluation index values ​​constitute an evaluation index value set, and the evaluation index value set includes B evaluation indicators, preferably, B is 3, and the evaluation combination of the stable support capacity of the receiving-end power grid includes short-circuit ratio (new energy site short-circuit ratio), damping ratio and transient power transmission capacity (simplified transient power transmission capacity), and the evaluation index value set includes a short-circuit ratio improvement margin value set, a damping ratio improvement margin value set and a transient power transmission capacity improvement margin value set, that is, in some preferred embodiments, each planning scheme includes three evaluation index values: short-circuit ratio improvement margin, damping ratio improvement margin and transient power transmission capacity improvement margin;

[0128] Specifically, based on the planning scheme set, the damping ratio and transient power transmission capacity are obtained through small disturbance or transient simulation according to the simplified model of the AC system;

[0129] The calculation formula of short circuit ratio improvement margin α is:

[0130]

[0131] In the formula, α is the short-circuit ratio improvement margin, MRSCR is the short-circuit ratio, and MRSCR0 is the initial short-circuit ratio;

[0132] The calculation formula of the damping ratio improvement margin β is:

[0133]

[0134] In the formula, β is the damping ratio improvement margin, ξ is the damping ratio, and ξ0 is the initial damping ratio;

[0135] The calculation formula of transient transmission capacity improvement margin γ is:

[0136]

[0137] Where γ is the transient transmission capacity improvement margin, sBTTCk is the transient transmission capacity, sBTTC k0 is the initial transient power transmission capacity;

[0138] Based on the planning scheme set, the evaluation index value set is calculated and obtained according to the simplified model of the AC system and the new energy grid-connected system;

[0139] Based on the planning scheme set and the evaluation index value set, the TOPSIS method of the entropy weight method is used to screen the planning scheme set to obtain the optimal planning scheme; the steps are:

[0140] An original evaluation matrix is ​​constructed based on the planning scheme set and the evaluation index value set, wherein the number of rows of the original evaluation matrix is ​​the number of planning schemes in the planning scheme set, and the number of columns of the original evaluation matrix is ​​the types of evaluation indicators in the evaluation index value set;

[0141] The original evaluation matrix is ​​processed by forward and normalization to obtain the evaluation matrix W; the evaluation matrix W is:

[0142]

[0143] In the matrix, the row data represents the different evaluation index values ​​under the same planning scheme, and the column data represents the evaluation index values ​​of different schemes under the same evaluation index;

[0144] The indicator weight value set is obtained by calculating the evaluation matrix. The indicator weight is the ratio of an evaluation indicator under one planning scheme to the sum of the same evaluation indicators under all planning schemes. The indicator weight is:

[0145]

[0146] In the formula, p ab is the weight of the bth evaluation indicator under the ath planning scheme, w ab is the bth evaluation index value under the ath planning scheme, b is the evaluation index number, and φ is the total number of planning schemes in the planning scheme set;

[0147] The information entropy value set is obtained according to the indicator weight value set. The information entropy value set includes the information entropy of the same evaluation indicator. The information entropy is:

[0148]

[0149] In the formula, E b is the information entropy of the b-th evaluation index; set: when p ab = 0, p ab lnp ab =0;

[0150] The weight value set is obtained according to the information entropy value set, and the weight value set includes the weights of the same evaluation indicator; the weights are:

[0151]

[0152] In the formula, F b is the weight of the bth evaluation indicator;

[0153] The evaluation matrix is ​​weighted according to the weight value set to obtain a weighted evaluation matrix, wherein the weighted evaluation matrix includes weighted evaluation values; the weighted evaluation matrix Z is:

[0154]

[0155] Obtaining maximum and minimum values ​​according to the weighted evaluation matrix;

[0156] Define the maximum value as: Z + =(max{z 11 ,z 21 ,…,z φ1},max{z 12 ,z 22 ,…,z φ2},max{z 13 ,z 23 ,…,z φ3}), the minimum value is: Z - =(min{z 11 ,z 21 ,…,z φ1},min{z 12 ,z 22 ,…,z φ2},min{z 13 ,z 23 ,…,z φ3});

[0157] Right now is the maximum value of the bth evaluation index, is the minimum value of the bth evaluation index;

[0158] A first distance value set is obtained according to the weighted evaluation matrix and the maximum value, where the first distance is the distance between the weighted evaluation value and the maximum value; the first distance is:

[0159]

[0160] In the formula, is the first distance of the a-th planning scheme, z ab is the weighted evaluation value of the bth evaluation indicator under the ath planning scheme, the value of the ath row and bth column in the weighted evaluation matrix Z;

[0161] A second distance value set is obtained according to the weighted evaluation matrix and the minimum value, where the second distance is the distance between the weighted evaluation value and the minimum value; the second distance is:

[0162]

[0163] In the formula, is the second distance of the a-th planning scheme;

[0164] The evaluation value set of the planning scheme set is obtained according to the first distance value set and the second distance value set; the evaluation value is:

[0165]

[0166] In the formula, S a is the evaluation value of the a-th planning scheme, is the first distance of the a-th planning scheme, is the second distance of the a-th planning scheme, and a is the sequence number of the planning scheme in the planning scheme set;

[0167] Sorting the planning schemes according to the evaluation value set and obtaining the sorting result;

[0168] By S a The planning scheme that is closest to the optimal solution and farthest from the worst solution can be obtained, that is, S a The minimum corresponding planning scheme, the optimal grid planning scheme is obtained according to the sorting results, that is, the optimal planning scheme.

[0169] Calculation example:

[0170] Taking a DC feed-in system with a high proportion of renewable energy in a certain area as an example, the main grid structure of the renewable energy grid-connected system is as follows: Figure 2 As shown, the total output of new energy in the new energy grid-connected system is 1800MW, the total output of thermal power is 1553MW, and the DC power is 1600MW. The main grid has a total of 66 nodes, including 24 500kV AC lines and 12 220kV AC lines. The load is relatively evenly distributed throughout the grid, and the dynamic model of the load is 30% induction motor + 70% constant impedance load.

[0171] The original nodes and line parameters of the main grid of the new energy grid-connected system are extracted, and the original nodes of the main grid are freely combined in pairs to form a series of line sets. Considering the actual space or other constraints, the line set to be planned is screened.

[0172] The set of lines to be planned is the selection range of the lines to be planned. The location and number of the lines to be planned are used as decision variables. The cost of the data set of the lines to be planned and the short-circuit ratio improvement margin of the new energy multi-station are used as the objective function. The non-dominated sorting genetic algorithm with elite strategy (NSGA-II) is used to solve the receiving-end power grid planning model to obtain the Pareto solution set and obtain the planning scheme set, such as Figure 3 shown.

[0173] The obtained planning scheme set is simulated, and the original new energy grid-connected system grid is used as a comparison scheme. The fault scenario is set for transient analysis. Preferably, the fault scenario is set to "N-1 fault". The fault scenario of this example is set as a double-circuit line between nodes B03 and B05. A three-phase permanent short circuit occurs in one line at t=1.00s, and the circuit breaker on the B03 side of the fault line trips at t=1.09s. The circuit breaker on the B05 side of the fault line trips at t=1.10s, and the other parallel line is cut off at the same time. The initial short-circuit ratio, initial damping ratio and initial transient transmission capacity under the original new energy grid-connected system grid are calculated respectively as shown in Table 1:

[0174] Table 1 Initial short-circuit ratio, initial damping ratio and initial transient transmission capacity of the original new energy grid-connected system

[0175]

[0176] The voltage curve and power angle curve of each planning scheme after the fault occurs are obtained by simulation. The voltage curve and power angle curve of some planning schemes are as follows: Figures 4 to 9 At the same time, the evaluation index of each planning scheme is calculated. In this example, the evaluation index of each planning scheme includes the short-circuit ratio improvement margin, the damping ratio improvement margin and the transient transmission capacity improvement margin. The evaluation indexes of some planning schemes are shown in Table 2:

[0177] Table 2 Evaluation indicators of the planning scheme

[0178]

[0179] The TOPSIS method based on entropy weight method is used to analyze various evaluation indicators. The scheme with the best comprehensive index is taken as the optimal planning scheme. The optimal planning scheme is shown in Table 3. The system voltage curve and power angle change curve under the optimal planning scheme are shown in Table 3. Fig.10 , Fig.11 shown.

[0180] Table 3 Optimal planning scheme

[0181]

[0182] A device for realizing a grid planning method for improving the multi-dimensional stable support capability of a receiving-end power grid, comprising a data input unit, a data analysis unit and an optimization algorithm unit;

[0183] Data input unit:

[0184] Used to obtain main grid node and grid line data sets based on new energy grid-connected systems;

[0185] Used to obtain a simplified model of the AC system for connecting new energy sources based on the main grid node and grid line data set using a multi-port Thevenin equivalent method;

[0186] In actual application, it can also be used for real-time monitoring of data;

[0187] Data Analysis Unit:

[0188] Used to construct a receiving-end power grid planning model based on a simplified AC system model;

[0189] Used to solve the receiving-end power grid planning model using NSGA-II to obtain a planning solution set;

[0190] Optimization algorithm unit:

[0191] It is used to calculate and obtain the evaluation index value set based on the planning scheme set, according to the simplified model of the AC system and the new energy grid-connected system;

[0192] It is used to screen the planning scheme set based on the planning scheme set and the evaluation index value set, and obtain the optimal planning scheme by using the TOPSIS method of the entropy weight method.

[0193] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.

Claims

1. A grid planning method for improving the multi-dimensional stable support capability of a receiving-end power grid, characterized by: The following steps are involved: Based on the new energy grid-connected system, obtain the main grid node and grid line data set; The grid line data set includes an original line data set and a line data set to be planned; A simplified model of an AC system for connecting new energy sources is obtained based on the main grid nodes and the grid line data set using a multi-port Thevenin equivalent method; A receiving-end power grid framework planning model is constructed based on the simplified model of the AC system, wherein the receiving-end power grid framework planning model includes decision variables and objective functions, wherein the decision variables are the line data set to be planned, and the objective function includes a cost minimization function and an evaluation index maximization function of the line data set to be planned, and the evaluation index is an index for measuring the stability of the power grid; Using NSGA-II to solve the receiving-end power grid planning model to obtain a planning scheme set; Based on the planning scheme set, according to the simplified model of the AC system and the new energy grid-connected system, respectively calculate and obtain an evaluation index value set; Based on the evaluation index value set, the TOPSIS method of the entropy weight method is used to screen and obtain the optimal planning scheme from the planning scheme set.

2. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 1 is characterized in that: The grid stability includes voltage stability, frequency stability and power angle stability.

3. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 1 is characterized in that: The steps of obtaining the simplified model of the AC system are: Obtaining a node-line association matrix and a line admittance matrix according to the main grid nodes and the original line data set; Obtaining an original line vector according to the original line data set; Obtaining a route vector to be planned according to the route data set to be planned; Obtaining a first diagonal matrix by calculating a diagonal matrix after adding the original line vector and the line vector to be planned; Obtaining a second diagonal matrix by calculating a diagonal matrix according to the line admittance matrix; Obtaining a node-line transposed matrix according to the node-line association matrix transposed; Obtaining a node admittance matrix by multiplying the node-line association matrix, the first diagonal matrix, the second diagonal matrix, and the node-line transposed matrix; Obtaining an original impedance matrix by inverting the node admittance matrix; The simplified model of the AC system is obtained according to the original impedance matrix using a multi-port Thevenin equivalent method.

4. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 3 is characterized in that: The cost of the line data set to be planned includes a one-time investment fee and an operation and maintenance fee.

5. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 4 is characterized in that: The cost minimization function is: In the formula, min is the minimization function, f1 is the cost function, C1 is the primary investment cost, C2 is the operation and maintenance cost, x is the line vector to be planned, l is the distance vector of the transmission line, r is the distance between the transmission lines and the transmission lines. D is the discount rate, S L For the service life.

6. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 1 is characterized in that: Based on the evaluation index value set, the steps of using the TOPSIS method of the entropy weight method to screen the planning scheme set to obtain the optimal planning scheme are as follows: Constructing an original evaluation matrix based on the planning scheme set and the evaluation index value set, wherein the number of rows of the original evaluation matrix is ​​the number of planning schemes in the planning scheme set, and the number of columns of the original evaluation matrix is ​​the types of evaluation indicators in the evaluation index value set; Performing forward and standardization processing on the original evaluation matrix to obtain an evaluation matrix; Obtaining an indicator weight value set according to the evaluation matrix, where the indicator weight is the ratio of an evaluation indicator under one planning scheme to the sum of the same evaluation indicators under all planning schemes; Obtaining an information entropy value set according to the indicator weight value set, wherein the information entropy value set includes the information entropy of the same evaluation indicator; Obtaining a weight value set according to the information entropy value set, wherein the weight value set includes weights of the same evaluation indicator; Performing weighted processing on the evaluation matrix according to the weight value set to obtain a weighted evaluation matrix, wherein the weighted evaluation matrix includes weighted evaluation values; Obtaining maximum and minimum values ​​according to the weighted evaluation matrix; Obtaining a first distance value set according to the weighted evaluation matrix and the maximum value, wherein the first distance is the distance between the weighted evaluation value and the maximum value; Obtaining a second distance value set according to the weighted evaluation matrix and the minimum value, wherein the second distance is the distance between the weighted evaluation value and the minimum value; Obtaining an evaluation value set of the planning scheme set according to the first distance value set and the second distance value set; Sorting the planning schemes according to the evaluation value set and obtaining a sorting result; The optimal planning scheme is obtained according to the sorting result.

7. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 6 is characterized in that: The evaluation value is: In the formula, S a is the evaluation value of the a-th planning scheme, is the first distance of the a-th planning scheme, is the second distance of the a-th planning scheme, and a is the sequence number of the planning scheme in the planning scheme set.

8. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to any one of claims 1 to 7, characterized in that: The evaluation index value set includes a short-circuit ratio improvement margin value set, a damping ratio improvement margin value set and a transient power transmission capacity improvement margin value set.

9. The grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid according to claim 8 is characterized in that: The short circuit ratio improvement margin is obtained according to the short circuit ratio of the new energy station and the initial short circuit ratio; the initial short circuit ratio is the initial short circuit ratio of the new energy grid-connected system; The short-circuit ratio of the new energy station is: Where, MRSCR i is the short-circuit ratio of the i-th renewable energy station, S aci is the three-phase short-circuit capacity of the grid access point corresponding to the i-th new energy station, P rei is the active power injected into the grid connection point of the i-th new energy station, Z eqij is the equivalent impedance from the jth renewable energy station to the ith renewable energy station, Z eqii is the equivalent self-impedance of the i-th new energy station, P rej is the active power injected into the grid-connected point of the j-th renewable energy station, i and j are the serial numbers of the renewable energy stations, and n is the total number of renewable energy stations.

10. A device for implementing the grid planning method for improving the multi-dimensional stable support capability of the receiving-end power grid as described in any one of claims 1 to 9, characterized in that: It includes a data input unit, a data analysis unit and an optimization algorithm unit; The data input unit: Used to obtain main grid node and grid line data sets based on new energy grid-connected systems; Used to obtain a simplified model of the AC system for connecting new energy sources according to the main grid nodes and the grid line data sets by using a multi-port Thevenin equivalent method; The data analysis unit: Used to construct a receiving-end power grid planning model based on the simplified model of the AC system; Used to solve the receiving-end power grid planning model using NSGA-II to obtain a planning solution set; The optimization algorithm unit: Used to calculate and obtain evaluation index value sets based on the planning scheme set, according to the simplified model of the AC system and the new energy grid-connected system; It is used to screen the planning scheme set based on the planning scheme set and the evaluation index value set by using the TOPSIS method of the entropy weight method to obtain the optimal planning scheme.

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